Tilo Kircher

Clarifying the patterns of neural activation during fear extinction in anxiety disorders (ADs) is essential for advancing our understanding of inhibitory learning. However, the underlying mechanisms remain poorly characterized in large, heterogeneous cohorts of patients. Utilizing a delayed fear extinction fMRI paradigm, this study examined neural responses in 280 patients with ADs — comprising panic disorder, agoraphobia, panic disorder with agoraphobia, social anxiety disorder, and multiple specific phobias — compared to 103 healthy controls (HC). The attenuation of neural activation to conditioned fear (CS+) and safety (CS−) stimuli from early versus late extinction was compared between groups. Subjective ratings of US-expectancy, arousal, and valence were also collected. In a whole-brain voxelwise analysis, patients showed significantly reduced attenuation of neural activation across both CS+ and CS− relative to HC from early compared to late phases of extinction learning. This pattern was observed in a broad network, encompassing the insular cortex (IC) and bilateral supplementary motor area (SMA), among other regions. No interaction between group and stimulus type was observed. Region-of-interest analysis of the left anterior insular cortex confirmed that reduced attenuation in this region represents a robust neural signature of altered extinction learning in ADs. Furthermore, patients reported persistently higher fear expectancy and arousal than HC, consistent with sustained alterations in threat-related processing following extinction training. Together, these findings highlight altered extinction-related neural attenuation as a transdiagnostic feature of ADs, offering novel insights into the neural mechanisms underlying fear extinction.

Background: Brain-wide association effects are commonly overestimated in psychiatric neuroimaging research, leading to low replicability and generalizability of findings, and ultimately to poor translation to clinical utility. Cross-validation is currently not standardly used in univariate neuroimaging analyses but could pose a potential counteracting measure for this problem. Methods: We systematically evaluated the benefit of a cross-validation procedure to expose inflated effect size estimates in mass-univariate voxel-based analysis, using body mass index (BMI) as an example predictor for gray matter. A total of n = 3401 MRI scans from a psychiatric cohort were used to investigate the impact of applying cross-validation on effect size estimates across a range of sample sizes and across different localizations with a range of underlying pooled effect sizes. Results: BMI was negatively associated with gray matter density with a maximum underlying effect size of R ² _p = 0.036 (in the full sample of n = 3401). However, without cross-validation, effects were substantially overestimated in small samples, with effect sizes ranging up to R ² _p = 0.535. Even with an underlying null-effect, effect size estimates up to R ² _p = 0.429 were reached in samples with n = 25. Applying cross-validation revealed that these inflated effects in small samples did not generalize to novel data. A minimum sample size of n = 100 was required for largest brain-wide effects to start generalizing, while samples with n = 400 were needed for smaller effects to generalize. This ‘point of initial generalizability’ was not reached for underlying null effects, even with the maximum available sample of n = 3401. Conclusions: Results suggest that cross-validation is an effective measure to expose overestimated effects in small samples and thus could help to counteract low replicability and generalizability in brain-wide mass-univariate association studies. Open-source code is provided for practical implementation.

Bipolar disorder (BD) is a prevalent psychiatric condition characterized by marked clinical and neuroanatomical heterogeneity. Prior neuroimaging studies have used case-control comparisons yielding small to moderate effect sizes for group differences. Large and diverse samples are needed to map heterogeneity within BD. Here, we used individualized brain morphometry deviations derived from normative modeling for three MRI-based measures (regional cortical thickness, regional surface area, and subcortical volumes). We applied this approach to a large international sample of 4 660 individuals (1 145 with bipolar I disorder and 3 515 healthy controls) from the ENIGMA Bipolar Disorder Working Group consortium (age range = 8–80, female = 53.15%) and examined the distribution of deviations within the bipolar disorder group, differences between healthy controls and individuals with bipolar disorder, and associations between neuroanatomical deviations and clinical variables. We found that individuals with bipolar disorder showed a higher proportion of localized extreme deviations, notably negative cortical thickness deviations in regions consistently reported as thinner in prior studies (the left superior frontal gyrus, bilateral inferior frontal gyrus (pars opercularis), left precuneus) and subcortical volume negative deviations in the left hippocampus. In addition, ROI-level deviations in the left middle temporal gyrus and the left inferior temporal gyrus were associated with longer illness duration. This is the largest study to date exploring individualized deviations as neuroanatomical features associated with bipolar disorder. Our findings suggest that neuroanatomical heterogeneity is a core feature of bipolar disorder, with preferential left-lateralized cortical-hippocampal involvement pointing to spatially structured heterogeneity.

Background: Although most individuals with schizophrenia do not show aggressive behavior, rates are higher than in the general population. Identifying the neurobiological correlates of aggression may inform prevention. Methods: We included 2260 individuals with schizophrenia spectrum disorders and 3058 healthy controls (HC) from 26 sites in the ENIGMA-Schizophrenia Working Group. The former were classified as showing at least moderate aggression (SZ+A; N = 231) or not (SZ−A; N = 2029) based on symptom rating scales. Structural covariance networks were constructed from cortical and subcortical volumes. We examined global and nodal network topology, as well as density within six large-scale brain networks and between each network and the rest of the brain. Metrics were summarized as the area under the curve (AUC) across a prespecified range of densities ( K = 0.10–0.25, Δ K = 0.01). Group differences were assessed using pairwise permutation tests with Benjamini–Hochberg correction for multiple comparisons. Results: Global and nodal network topology did not differ significantly between SZ+A and HC. Network–rest density of the salience network (SN) was higher in SZ+A than in HC, although this difference did not survive multiple-comparison correction (ΔAUC = 0.018, p _BH = .083). In exploratory subgroup analyses, higher SN network–rest density was observed only in SZ+A with high delusion severity (ΔAUC = 0.021, p = .007). By contrast, SZ−A demonstrated extensive alterations in global network topology compared with SZ+A and HC, alongside selective alterations in nodal topology and large-scale network density compared with HC. Results were broadly consistent across sensitivity analyses using alternative definitions of aggression and adjusting for current antipsychotic dose. Conclusions: SZ+A was characterized by fewer—not more—abnormalities in structural covariance network organization than SZ−A, which may reflect etiological heterogeneity. In SZ+A with high delusion severity, higher network–rest density of the SN may point to aberrant salience processing in aggressive behavior.

Background: Formal thought disorder (FTD) has been conceptualized as a categorical syndrome of psychosis, but this remains untested. Clarifying whether FTD is categorical or continuous and its supra-thres hold prevalence in non-clinical populations has direct implications for bears directly on mechanistic studies of FTD, psychosis prediction, and early intervention. Methods: In a multi-site cross-sectional study of 520 individuals (352 clinical, 168 non-clinical), we used the Thought and Language Index to assess FTD across three subconstructs: Loosening, Peculiarities , and Impoverishment. Using complementary taxometric procedures (MAMBAC, MAXEIG, and L-Mode) with simulation of comparison data and multiple thresholding approaches (minimally important change criteria, ROC analysis, intuitive binarization), we evaluated the latent structure of FTD, optimal cutoff points to estimate FTD prevalence, and the frequency of co-occurrence of subconstructs. Results: Taxometric analyses revealed a predominantly dimensional latent structure for FTD across various base rates. Among healthy individuals, Peculiarities were uncommon (1.2%), but 11.3% exhibited Loosening and 8.9% had Impoverishment even with conservative thresholds. Multi-construct co-occurrence exceeded chance across the sample but was restricted to patients at conservative clinical thresholds. Conclusions: FTD exists in a continuous dimensional structure extending into healthy populations. Each dimension is measurable in healthy individuals, but multi-dimensional convergence at clinical thresholds (i.e. disorder with functional impairment) appears specific to patients. This suggests that common vulnerability factors increase the likelihood of elevations across multiple but substantially independent dimensions of FTD. As a hypothesis requiring prospective validation, clinical risk of psychosis may depend on the coalescence rather than single-dimensional severity of FTD subconstructs.

Major depressive disorder (MDD) is increasingly conceptualized as a dysconnection syndrome involving widespread brain network disruptions. However, most neuroimaging studies rely on within-sample case–control comparisons, leaving unclear whether identified connectivity patterns generalize across populations or explain clinical variation within patients. In this cross-sectional observational study, we test whether a depression-related connectivity pattern from a Japanese cohort (824 controls, 229 MDD) generalizes to a German cohort (923 controls, 694 MDD) using polyconnectomic scores (PCS), a metric for quantifying whole-brain connectivity disturbances. PCS _MDD significantly distinguished patients from controls ( z = 5.1, P < 0.001), with the highest scores in acutely depressed patients. Its diagnostic signal was distributed across the connectome, with performance declining under stricter edge-level thresholds. PCS _MDD captured a spatially specific dysconnectivity pattern that explained variance beyond mean within- and between-network connectivity summaries. No associations were found between PCS _MDD and past disease course in MDD patients. By contrast, patients with higher PCS _MDD exhibited greater current symptom severity ( r = 0.09, P = 0.022), particularly loss of pleasure ( r = 0.12, P _FDR = 0.041). These findings demonstrate that a depression-related connectivity pattern generalizes across populations and relates selectively to current symptom severity.

Resilience, the ability to adapt positively in the face of adversity, is shaped by combined influences of risk and protective factors. Previous neuroimaging studies on resilience have predominantly focused on single factors, often operationalizing resilience dichotomously as the absence of psychiatric disorders despite adversity. In this prospective magnetic resonance imaging study, we defined resilience as “better-than-expected” depressive symptom severity (Hamilton Depression Rating Scale) relative to cumulative risk across 22 risk and protective variables. Using ridge-regularized regression in N = 1804 participants (955 healthy, 849 depressed) from the Marburg-Münster Affective Disorders Cohort Study, we predicted symptom severity and derived residuals as measures of resilience. Residuals were then used to predict gray matter volume (GMV) and cortical thickness at baseline (T1) and two-year follow-up (T2; N = 808). This approach was complemented by extreme-group comparisons of resilient (better-than-expected outcome) and vulnerable (worse-than-expected outcome) individuals. Cumulative risk explained 49.6% of variance in depressive symptoms at T1 and 40.1% at T2. Residual scores showed moderate temporal stability ( r = 0.32, p < 0.001). Region-of-interest and whole-brain analyses revealed no morphometric associations with resilience at T1. In contrast, higher resilience at T1 predicted lower GMV in the left inferior orbitofrontal gyrus (IOFG) and temporal pole at T2 (ROI, p _FWE(peak) r _partial = 0.18), with no changes in cortical thickness. Taken together, resilience to cumulative risk, defined as better-than-expected depressive symptom severity, was not associated with immediate brain structural differences. However, prospective analyses revealed smaller GMV in the IOFG and temporal pole over time, potentially reflecting greater neural efficiency or delayed biological costs.

Background: Cognitive deficits in Major Depressive Disorder (MDD) are clinically debilitating, persist after remission, and predict worse long-term outcomes, yet factors determining which patients are most at risk remain poorly understood. Cognitive reserve (CR) – the brain's capacity to buffer negative effects of structural alterations on cognitive functioning – is assessable through brief, clinically applicable proxy measures and may serve as a practical stratification factor in MDD. Methods: CR of 539 MDD patients (65% female) and 549 healthy controls (65% female) from the Marburg-Münster Affective Disorders Cohort Study was estimated from educational years, occupational attainment, and verbal IQ. We examined the association of CR with cognitive impairment, tested whether CR moderates the relationship between MRI-based connectome alterations and cognitive performance, and predicted two-year disease course characteristics from baseline CR, benchmarked against polygenic risk and childhood maltreatment. Results: Low CR was associated with increased cognitive impairment, especially in MDD patients (OR=6.24, p=0.003). CR moderated the association between cognitive performance and white matter network connectivity (t=2.780, pFWE=0.006). Low baseline CR predicted increased episode duration and symptom severity (psFDR<0.001) over two years, with effect sizes statistically indistinguishable from those of polygenic risk and childhood maltreatment. Conclusions: CR identifies MDD patients at risk for cognitive impairment and worse disease progression, with prognostic utility comparable to polygenic risk and childhood maltreatment. The moderating effect of CR on the brain network-cognition association provides a neurobiological rationale for this vulnerability. These findings support incorporating CR into routine clinical assessment to guide risk stratification and personalized treatment planning.

Major depressive disorder (MDD) has been associated with accelerated structural brain aging, yet whether this reflects a pre-existing neurobiological vulnerability, a dynamic acute state effect, or an accumulating biological residual remains unresolved. Across two longitudinal cohorts (N=3220), including a unique sample of 78 initially healthy individuals who transitioned into their first depressive episode during the study course, we systematically tested all three hypotheses. Patients with diagnosed MDD showed elevated MRI-derived brain age relative to healthy controls (1.4 and 2.5 years across cohorts). For the vulnerability hypothesis, individuals scanned prior to their first episode showed no baseline elevation, despite already demonstrating subclinical elevations in self-reported symptom severity, indicating that advanced brain age does not precede illness onset. For the state hypothesis, we found no acceleration of brain aging following the first depressive episode, and longitudinal brain age trajectories were independent of acute clinical symptom severity. Finally, neither episode duration nor recurrence scaled with brain age. Accelerated brain aging in depression is therefore neither an antecedent vulnerability nor an acute state marker of the first episode, but rather a stable biological feature of a long term illness course.

Background: The gut microbiome has been linked to major depressive disorder (MDD), yet it remains unclear whether antidepressant treatment influences these associations. This study aimed to clarify the role of serotonin reuptake inhibitors (SSRI/SNRI) in shaping gut microbiome changes observed in MDD. Methods: We conducted cross-sectional analyses in two independent patient cohorts (total N = 1802) and a meta-analysis across both cohorts, comparing the gut microbiome of MDD patients with and without SSRI/SNRI treatment. Results: Here we show that SSRI/SNRI treatment is consistently associated with reduced Clostridium sensu stricto 1 abundance. This effect is specific to SSRI/SNRI treatment and not observed with other psychotropic medications. Importantly, reductions in Clostridium sensu stricto 1 in MDD compared to unaffected controls are explained by SSRI/SNRI medication status. Conclusions: Antidepressant treatment is an important factor shaping gut microbiome alterations linked to MDD, underscoring the need to account for medication effects and potentially informing future microbiome-based strategies to improve treatment response.